Method for the non-destructive testing of the volume of a test object and testing device configured for carrying out such a method

ABSTRACT

A method for the non-destructive testing of the volume of a test object, during the course of which a volume raw image of the test object is recorded by a suitable non-destructive imaging testing method. Then, those regions of the volume raw image are identified that are not to be attributed to the test object material. It is checked whether an identified region is completely embedded in regions that are to be associated with the test object material. If necessary, such a region is assimilated to those regions that are to be associated with the test object material, forming a filled volume raw image. Finally, a difference is generated between the volume raw image and the filled volume raw image, forming a first flaw image.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 14/765,757, filed Aug. 4, 2015, entitled “Method for theNon-Destructive Testing of the Volume of a Test Object and TestingDevice Configured for Carrying Out Such a Method,” which is a U.S.National Stage of International Patent Application No.PCT/EP2014/052033, filed Feb. 3, 2014, which claims the benefit ofGerman Patent Application No. 102013001808.0, filed Feb. 4, 2013, theentirety of each of which is hereby incorporated by reference.

FIELD

Embodiments of the present invention relate to the field of theautomated non-destructive testing of the volume of a test object bymeans of suitable testing methods, such as ultrasonic testing, eddycurrent testing or X-ray testing. An embodiment relates to a method forthe non-destructive testing of the volume of a test object as well as toa testing device configured for carrying out such a method. Inparticular, a three-dimensional image of the test object can be formedby means of the method according to an embodiment of the invention, inwhich detected flaw indicators are registered that can be associatedwith the test object volume. The three-dimensional image can be thestarting point of further method steps for flaw analysis and possiblyflaw classification. In an embodiment, in an at least partially, and inan embodiment fully, automated manner a classification of the examinedtest object as “In order”/“Not in order” in accordance with predefinedclassification parameters is offered. Moreover, the method according toan embodiment can be designed to be adaptive in order to adapt in aself-learning manner to changed testing parameters.

BACKGROUND

A multitude of testing methods is known in the field of non-destructivematerial testing that are based on a comparison of test data, which wereobtained on a test object by means of a non-destructive testing method,with a CAD model of the test object. Due to the required CAD model ofthe test object, these testing methods can only be applied in aneconomical manner to test objects of which a larger number of identicalspecimens are to be tested, e.g. within the context of seriesproduction. Furthermore, an alignment of the actual test data to the CADmodel is necessary (a “registration”), which requires great computingpower.

SUMMARY

Embodiments of the present invention propose a method for thenon-destructive testing of the volume of a test object which is suitablefor real-time testing of, for example, series-produced parts.Embodiments of the present invention propose a testing device suitablefor carrying out such a method.

Embodiments of the present invention provide a method and a testingdevice. The features of the dependent claims, and of the describedembodiments, can be combined with one another within the context of whatis technically feasible, even if this is not explicitly describedhereinafter. This also applies to a combination of method and deviceclaims.

In an embodiment, the method and the testing device are based upon theformation of volume raw images of a test object by means of X-raycomputer tomography. Recording three-dimensional X-ray tomography imagesinline, e.g. within a production line, has become possible at the timeof the filing of this application thanks to CT scanners working fullyautomatically that are fed test objects in an automated manner. Inparticular, an embodiment relates to analyzing, also inline, moreparticularly in real time, the volume raw images of the scanned testobjects produced e.g. in such a CT method and make them usable for flawor test object classification.

A method according to an embodiment comprises at least the followingmethod steps:

a. recording a volume raw image of the test object by means of asuitable non-destructive imaging testing method, such as X-ray computertomography,

b. identifying the regions of the volume raw image that are not to beattributed to the test object material (hereinafter “dark regions”),e.g. by means of gray-value acquisition and threshold value analysis,

c. checking the identified dark region as to whether it is completelyembedded in regions that are to be associated with the test objectmaterial (hereinafter “light regions”), and, if necessary, assimilatingsuch a dark region to the surrounding light regions, forming a filledvolume raw image or a data set on which that is based, and

d. generating the difference between the volume raw image and the filledvolume raw image, forming a first flaw image or a data set on which thatis based.

All of the images formed within the context of the method constitutegraphic representations of data sets that include the test object volumecompletely or at least partially. The image processing steps discussedabove are therefore typically carried out with the underlying data sets.The graphic representations primarily serve for illustrating the testresult to a human operator of a testing device configured according toan embodiment of the invention. Hereinafter, the terms “image” or“graphic representation” on the one hand, and “data set” on the otherhand, are used substantially synonymously.

In an embodiment, the method for forming a first flaw image can becarried out in a fully automated manner and provides a reliabledetection of flaw indications caused by larger flaws, such as inclusionsof air, piping etc. in the test object volume. The detected flaws canthen be subjected to a partially, or in an embodiment fully, automatedflaw classification by means of suitable methods, such as thresholdanalyses etc, or serve as starting points (“seed points”) for the“region-growing” method known from the prior art, or for its developmentaccording to the paper “Interactive Volume Exploration for FeatureDetection and Quantification in Industrial CT Data” by Markus Hadwigeret al. in IEEE Transactions on Visualisation and Computer Graphics, Vol.14, No. 6 (November/December 2008), 1507-1514.

In an embodiment, the volume raw image is additionally processed asfollows:

e. applying a filter algorithm, such as a cutoff filter or medianfilter, for amplifying possible flaw indicators, forming a filteredvolume raw image, and

f. limit value generation of the filtered volume raw image, forming asecond flaw image or a data set on which that is based.

This development forms a second flaw image which also contains flawindications of flaws that only produce weaker signals in the volume rawimage, for example due to their small size or also due to a density thatis only slightly different locally, so that possibly they are notcontained in the first flaw image according to embodiments of thepresent invention. In particular, it is possible, in connection withforming the filtered volume raw image, to also carry out a subtractionof the volume raw image in addition to the application of suitablefilter algorithms.

In an embodiment, the first flaw image and the second flaw image aremerged into a combined flaw image or a data set on which that is based.

In an embodiment, in the combined flaw image, those regions aresuppressed that are not associated with the test object material in thevolume raw image, for example due to their local gray value. Thus, flawindications that are not attributable to the test object, but are, forexample, artifacts of the image-forming testing method, can be reliablysuppressed. On the one hand, this results in an improvedinterpretability of the graphic representation of the result of themethod according to embodiments of the present invention, on the otherhand, this offers advantages in a subsequent flaw detection andclassification carried out in a partially or fully automated manner.

In addition to the X-ray computer tomography already mentioned, thenon-destructive imaging testing method can also be a tomography methodbased on ultrasound or eddy currents.

The method is particularly suitable for testing series-producedworkpieces, e.g. within the context of an inline real time inspection.

An embodiment permits the suppression of supposed flaw indications thatare actually not correlated to structures of a series-produced testobject that are to be considered flawed. Such flaw indications can be,for example, artifacts of the testing method used for image forming. InX-ray tomography, reflections on (inner) boundary surfaces of the testobject are observed, for example, which can lead to local shading. Theattenuation of the X-radiation in the test object material alsoresults—at least in test objects having a larger volume or consisting ofhighly absorbing materials—in a drop in the X-ray intensity towards thecenter of the test object. But also production-related faults, which,for example in casting processes, can often occur always at the samepositions and which can be counteracted by means of, for example, asuitable local dimensioning of the test object, can constitute such flawindications that one would like to disregard in a flaw assessment. Forthis purpose, the method according to an embodiment is developed bymeans of the following additional method steps:

g. forming a volume reference image from volume raw images of one ormore test object(s) which was/were classified to be “In order” on thebasis of predefined test criteria,

h. generating the difference between, on the one hand, one of the firstflaw image, the second flaw image or the combined flaw image and, on theother hand, the volume reference image.

Naturally, the volume reference image contains information on theabove-mentioned structures that are to be suppressed. A subtraction ofthis volume reference image from the first, second or combined flawimage therefore specifically eliminates these undesired structures. Inan embodiment, the method further comprises a registration step in whicha registration of a volume raw image to a volume reference image or to a3D CAD model of the test object takes place. Alternatively, or alsoadditionally, the method can also comprise a registration of the volumereference image to a 3D CAD model of the test object. In an embodiment,a registration takes place of both the volume raw image as well as thevolume reference image to a 3D CAD model of the test object.

Other embodiments of the method according to the invention are explainedin the context of the exemplary embodiments, which are to be understoodto be examples and non-limiting. In particular, this means that themethod-related features disclosed there can be combined, within thecontext of what is technically possible, with individual or several ofthe features discussed above as well as with the embodiments ordevelopments.

Embodiments of the device according to the invention are discussed belowin a cursory manner. With regard to the mode of operation as well as tothe advantageous effects of the device in its various disclosedembodiments as well as to the comprised features, reference is made tothe above discussion of the method according to the embodiments of theinvention, which can be directly transferred to the equivalent devicefeatures.

If a device according to embodiments of the present invention issuitable for recording and processing three-dimensional images of a testobject and possibly comprises components for this purpose, which areseparately designated in the following, these components can be realizedas software-based parts of a computer-based system. The computer-basedsystem can be a commercially available, operating system-based PC systemsuitable for running freely programmed application programs.Alternatively, the individual components can also be designed asmicrocode for freely programmable microprocessors, or also ashardware-implemented component members. Particularly in the case ofcomputationally intensive operations, the latter can have advantageswith regard to speed.

A testing device according to embodiments of the present invention forthe non-destructive testing of the volume of a test object comprises atleast the following features:

a. an image forming unit for recording a volume raw image of the testobject by means of a suitable non-destructive imaging testing method,

b. an image processing unit for the further processing of a volume rawimage which is configured to:

-   -   i. identify regions of the volume raw image that are not to be        attributed to the test object material,    -   ii. check an identified region as to whether it is completely        embedded in regions that are to be associated with the test        object material, and, if necessary, assimilate this region to        those regions that are to be associated with the test object        material, forming a filled volume raw image, and    -   iii. forming a first flaw image while generating the difference        between the volume raw image and the filled volume raw image.

In this case, the image forming unit can, in particular, be configuredto form volume raw images of a test object by means of a tomographymethod based on X-radiation, ultrasound or eddy currents. In particular,it can be configured to carry out an inline inspection ofseries-produced components, for which purpose it has a largely or fullyautomated manipulator unit for feeding the test objects to the imageforming unit. In particular, the image forming unit can be an X-raycomputer tomograph configured for forming three-dimensional testobjects.

In an embodiment, the image processing unit is configured to furtherprocess the volume raw image as follows:

c. applying a filter algorithm, such as a cutoff filter or medianfilter, for amplifying possible flaw indicators, forming a filteredvolume raw image, and

d. limit value generation of the filtered volume raw image, forming asecond flaw image.

In another embodiment, the image processing unit is configured to mergethe first flaw image and the second flaw image into a combined flawimage.

Generally, the testing device can comprise a display unit on which theformed flaw images can be displayed to an operator.

In another embodiment, the image processing unit of the testing deviceis configured to suppress in the combined flaw image those regions thatare not to be associated with the test object material in the volume rawimage.

Moreover, the image processing unit can be configured to carry out asubtraction of the volume raw image in order to form the filtered volumeraw image.

In another embodiment, the testing device according to the inventionmoreover comprises a classification unit configured to classify a testobject as being “In order”/“Not in order” on the basis of predefinedtest criteria. Such a classification can be made, for example, by meansof a partially or fully automated inspection of flaw indications in thefirst, second or combined flaw images formed according to an embodimentof the invention. If necessary, an additional intervention by the usercan also be provided.

In an embodiment, the image processing unit of the testing device isfurthermore configured to:

e. form a volume reference image from volume raw images of one or moretest object(s) which was/were classified to be “In order” on the basisof predefined test criteria, and

f. generate the difference between, on the one hand, the first flawimage, the second flaw image or the combined flaw image and, on theother hand, the volume reference image.

In an embodiment, the testing device further comprises a registrationunit configured to carry out at least one of the two followingregistration steps:

g. registration of a volume raw image to a volume reference image or toa 3D model of the test object, or

h. registration of the volume reference image to a 3D model of the testobject.

BRIEF DESCRIPTION OF THE DRAWINGS

Other advantages and features of the various embodiments of the testingdevice according to the invention are also apparent from the followingexemplary embodiments. It is pointed out that the method featuresdiscussed there can be directly transferred to the device according tothe invention to the extent it is characterized, within the context ofthe present invention, as carrying out certain process steps.

The exemplary embodiments will be explained with reference to thedrawing, in which the Figures show the following:

FIG. 1 is a first exemplary embodiment of a method according to theinvention,

FIG. 2 is an exemplary section through a test object's volume raw imageto be processed,

FIG. 3 is a section through a cleaned-up combined flaw image of the testobject obtained according to an embodiment of the invention, with thesection shown corresponding to the section from FIG. 2,

FIG. 4 is a cleaned-up combined three-dimensional flaw image of the testobject obtained according to an embodiment of the invention, accordingto FIG. 2, and

FIG. 5 is a schematic view of a method according to an embodiment of theinvention.

DETAILED DESCRIPTION

FIG. 1 schematically shows the process flow of an embodiment of a methodaccording to the invention. In this embodiment, the method is applied toa three-dimensional volume raw image obtained by means of X-ray computertomography on a pump housing formed as a casting. The starting point ofthe method is in Block 100 a three-dimensional volume raw image of thepump housing. This is subjected in Block 101 to a method step which isreferred to in the context of the present invention as segmentation.During segmentation, those regions of the volume raw image areidentified that are not to be attributed to the test object material.The basis for such an association can be, for example, an analysis ofthe gray values in the volume raw image including a threshold analysis.

However, the problem with an association made in this manner is thatlarger faults, piping or included gas bubbles in the test object, forexample, locally cause a gray value in the volume raw image that matchesthe gray value of the air surrounding the test object. In the case ofthese flaw regions, there is therefore the danger that they are notattributed to the test object volume. In the segmentation step, thevolume raw image is therefore analyzed in order to identify thoseregions in the volume raw image that are not to be attributed to thetest object material. In a subsequent processing step, the regions ofthe volume raw image identified herein are checked as to whether theyare completely embedded in regions that are to be associated with thetest object material. In a process step subsequent thereto, theidentified regions that are completely embedded in the test objectmaterial are assimilated to those regions that are to be associated withthe test object material, for example, by associating a medium grayvalue of the test object volume with them. By assimilating theidentified regions to the surrounding regions to be associated with thetest object material, a filled volume raw image is formed in step 102.

In the subsequent method step in Block 103, a difference is generatedbetween the volume raw image according to Block 100 and the filledvolume raw image according to Block 102, and results in a first flawimage from which first flaw indications are apparent.

At the same time, the volume raw image from Block 100 is subjected tofurther processing steps in a second branch of the method. In Block 201,the volume raw image is filtered, for example by means of a cutofffilter or median filter. In the subsequent processing step 202, adifference is generated between the volume raw image from Block 100 andthe filtered volume raw image from Block 201. Subsequent thereto, inprocess step 203, a limit value is generated of the filtered differenceimage from block 202, whereby flaw indications that do not exceed apredefined level are blanked out. Second flaw indications, which arecorrelated to smaller flaws in the test object material, remain. Thesensitivity of the method according to an embodiment of the inventioncan be influenced to a substantial extent by the selection of thethreshold used here. In Block 204, a second flaw image is provided as aresult of this second processing branch.

Then, the first flaw image (Block 104) from the first processing branchand the second flaw image (Block 203) from the second processing branchare merged into a combined flaw image, which takes place in step 301.

In the subsequent masking step according to Block 302, those regions ofthe flaw image are masked in the combined flaw image according to Block301 that are not to be attributed to the test object volume. Here, thefilled volume raw image from Block 102 can be used. It is possible toextract from the filled volume raw image, e.g. by means of thresholdanalysis, which image regions are to be attributed to the test objectvolume. By means of this masking step, all flaw indications areeliminated that are outside the test object, which therefore must beartifacts. The result of this masking step is a cleaned-up combined flawimage in Block 303, which can then be subjected to subsequent processingsteps, for example for a partially or fully automated flaw detection andclassification.

FIG. 2 shows a sectional view through the test object, which wasobtained by means of a volume raw image according to Block 100 servingas the starting point of the method according to an embodiment of theinvention.

FIG. 3 now shows an equivalent sectional view of the same test objectthat was obtained on the cleaned-up combined flaw image according toBlock 303, which is the result of the method according to an embodimentof the invention. It is very clear that the number of the flawindications apparent from the sectional view is considerably increasedin FIG. 3 compared to FIG. 2. At the same time, all regions of the imagesituated within the outline of the test object are associated with thetest object material, so that all flaw indications in FIG. 3 can besubjected to further automated processing. Therefore, the methodaccording to an embodiment of the invention permits reliably avoidingsuch flaws that are caused by flaw indications above a certain size nolonger being attributed to the test object material.

FIG. 4 now shows a perspective view of a combined flaw image of the pumphousing, of which FIGS. 2 and 3 show sectional views. The illustrationaccording to FIG. 4 and the underlying data were obtained by asuperposition of the cleaned-up combined flaw image according to Block303, which was obtained according to an embodiment of the invention,with the filled volume raw image according to Block 102, which was alsoobtained during the course of the method according to an embodiment ofthe invention. On the one hand, the structure of the examined testobject is clearly apparent from this combined illustration, on the otherhand, the flaw indications are easily recognizable to an operator of atesting system configured according to an embodiment of the invention.Furthermore, in the data set which is the basis of the illustrationaccording to FIG. 4, the flaw indications can very easily be subjectedto an automated inspection and classification.

FIG. 5 shows an embodiment of the method according to the invention,which comprises the method according to FIG. 1. This embodiment permitsthe effective suppression of artifacts in a volume raw image of a testobject that were caused by the non-destructive image forming testingmethod used. A requirement in order for the developed method to becapable of being carried out is that reference volume raw images areprovided of a plurality of test objects, which were inspected, forexample, by means of other non-destructive testing methods and whichwhere classified as being “In order” within the context of the testingtask to be carried out. This plurality of reference volume raw images isregistered in step 402 to a reference model, for example athree-dimensional CAD model of a test object. Within the context of thepresent invention, registration means that the volume raw imagesobtained from actual test objects are aligned to a reference model ofthe test object. This registration is essential for the developed methodto succeed, because here, volume raw images are to be compared with eachother that were obtained on different test objects, which did notinevitably had to have the same orientation in space during therecording of the reference volume images, due to the testing methodused. The result of the registration step 402 is a plurality ofregistered, i.e. aligned to a reference model, reference volume rawimages of different test objects that were classified as being “Inorder”.

In the subsequent processing step 403, the registered reference volumeraw images, if necessary, are subjected to a suitable filtering, suchas, again, a cutoff filter or median filter, in order to have structuresthat possibly exist come out more clearly.

In the subsequent processing step 404, an averaging process is carriedout over the plurality of the registered, optionally filtered, referencevolume raw images in order to form an adaptive reference backgroundimage.

In step 601, a volume raw image of a test object to be classified isrecorded by means of a non-destructive imaging testing method, whichmatches the image-forming testing method used for recording thereference images in step 401. In the subsequent step 602, the recordedvolume raw image of the test object to be classified is aligned in aregistration step in Block 602 to the reference model according to Block500. The aligned volume raw image formed here of the test object to beclassified is then subjected in Block 603 to the method according toFIG. 1. However, a difference is still generated here between the resultof the method according to FIG. 1, i.e. between the cleaned-up combinedflaw image according to Block 303, and the registered flaw referenceimage formed in step 404. In addition, a threshold analysis can becarried out on the formed difference image in order to suppress smallerdeviations from the reference image. Also in this case, the result ofthe method according to an embodiment of the invention can be influenceddecisively by the suitable selection of the threshold. Thebackground-corrected, cleaned-up, combined flaw image resulting in Block603 can then be subjected in Block 604 to a flaw classification, whichmay proceed, in particular, in a partially or fully automated manner.Using the result of this flaw classification in Block 604, a decidingunit can classify the analyzed test object as being “In order”/“Not inorder” in Block 605. On the one hand, this classification can take placefully automatically, on the other hand, an intervention by an operatoris also conceivable in this case.

Another embodiment of the method according to the invention providesthat the volume raw image that was obtained on a test object analyzed bymeans of the method discussed above and classified as being “In order”in Block 605 is added to the set of the reference volume raw imagesaccording to step 401. A self-learning system is thus developed in whichslowly changing influences, such as minor geometry changes of theanalyzed castings within the series due to ageing phenomena of the moldsused, or changed bubble inclusions, can be automatically compensated byslightly changed process parameters in casting processes.

Whilst exemplary and embodiments of the invention have been describedherein, the skilled person will appreciate that other embodiments arepossible and contemplated. The invention is intended to encompass allsuch embodiments that fall within the scope of the appended claims.

The invention claimed is:
 1. A method for the non-destructive testing,comprising: receiving a volume raw image of a test object captured by anon-destructive imaging testing method; identifying first dark regionsof the volume raw image having a first range of pixel values that arenot attributed to the test object material and first light regions ofthe raw volume image having a second range of pixel values that areattributed to the test object material; forming a filled raw volumeimage from the raw volume image by assigning a pixel value within thesecond range of pixel values to the first dark regions that arecompletely surrounded by first light regions; forming a first flaw imagefrom the difference between the volume raw image and the filled volumeraw image; forming a filtered raw volume image by applying a filter tothe raw volume image; forming a filtered difference image from thedifference between the raw volume image and the filtered raw volumeimage; forming a second flaw image by blanking out pixels from thefiltered difference image that have a pixel value less than a predefinedlevel; and forming a combined flaw image by merging the first and secondflaw images.
 2. The method of claim 1, wherein the filter is a cutofffilter or a median filter.
 3. The method of claim 1, wherein, in thecombined flaw image, the first dark regions of the volume raw image aresuppressed.
 4. The method of claim 1, further comprising forming acleaned-up combined flaw image by masking second dark regions of thecombined flaw image having the first range of pixel values that are notattributed to the test object material.
 5. The method of claim 4,wherein the masking is performed using a mask of the first light regionsextracted from the raw volume image.
 6. The method of claim 1, whereinthe non-destructive imaging testing method is a tomography method basedon X-rays, ultrasound, or eddy currents.
 7. A non-destructive testingmethod, comprising: receiving the combined flaw image formed accordingto claim 1, wherein the volume raw image is registered prior to formingthe first flaw image by alignment of the raw volume image to a referencemodel of the test object; registering a plurality of reference volumeraw images of test objects captured by a non-destructive testing,wherein the reference volume raw images are classified to be “In order”on the basis of predefined test criteria and wherein the registrationincludes alignment of the reference volume raw images to a referencemodel of the test object; forming a reference background image byaveraging the plurality of reference volume raw images; forming abackground corrected combined flaw image from a difference between thecombined flaw image and the reference background image; classifying thebackground corrected combined flaw image as being “In order” or “Not inorder according to the predetermined test criteria.
 8. The method ofclaim 7, wherein the reference model is a three-dimensional CAD model ofthe test object.
 9. The method of claim 7, further comprising addingbackground corrected combined flaw images classified as being “In order”to the plurality of reference volume raw images.
 10. A non-destructivetesting device, comprising: an image forming unit configured to record avolume raw image of the test object by a non-destructive imaging testingmethod; and an image processing unit configured to: identify first darkregions of the volume raw image having a first range of pixel valuesthat are not attributed to the test object material and first lightregions of the raw volume image having a second range of pixel valuesthat are attributed to the test object material; form a filled rawvolume image from the raw volume image by assigning a pixel value withinthe second range of pixel values to the first dark regions that arecompletely surrounded by first light regions; form a first flaw imagefrom the difference between the volume raw image and the filled volumeraw image; form a filtered raw volume image by applying a filter to theraw volume image; form a filtered difference image from the differencebetween the raw volume image and the filtered raw volume image; form asecond flaw image by blanking out pixels from the filtered differenceimage that have a pixel value less than a predefined level; and form acombined flaw image by merging the first and second flaw images.
 11. Thetesting device of claim 10, wherein the filter is a cutoff filter or amedian filter.
 12. The testing device of claim 10, wherein, in thecombined flaw image, the first dark regions of the volume raw image aresuppressed.
 13. The testing device of claim 10, wherein the imageprocessing unit is further configured to form a cleaned-up combined flawimage by masking second dark regions of the combined flaw image havingthe first range of pixel values that are not attributed to the testobject material.
 14. The testing device of claim 13, wherein the maskingis performed using a mask of the first light regions extracted from theraw volume image.
 15. The testing device of claim 10, wherein thenon-destructive imaging testing method is a tomography method based onX-rays, ultrasound, or eddy currents.
 16. The testing device of claim10, wherein the image processing unit is further configured to: receivethe combined flaw image formed according to claim 10, wherein the volumeraw image is registered prior to forming the first flaw image byalignment of the raw volume image to a reference model of the testobject; register a plurality of reference volume raw images of testobjects captured by a non-destructive testing, wherein the referencevolume raw images are classified to be “In order” on the basis ofpredefined test criteria and wherein the registration includes alignmentof the reference volume raw images to a reference model of the testobject; form a reference background image by averaging the plurality ofreference volume raw images; form a background corrected combined flawimage from a difference between the combined flaw image and thereference background image; classify the background corrected combinedflaw image as being “In order” or “Not in order according to thepredetermined test criteria.
 17. The testing device of claim 16, whereinthe reference model is a three-dimensional CAD model of the test object.18. The testing device of claim 16, wherein the image processing unit isfurther configured to add background corrected combined flaw imagesclassified as being “In order” to the plurality of reference volume rawimages.